Zhang Liang, Qu Yue, Jin Bo, Jing Lu, Gao Zhan, Liang Zhanhua
International Business College, Dongbei University of Finance and Economics, Dalian, China.
School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China.
JMIR Med Inform. 2020 Sep 16;8(9):e18689. doi: 10.2196/18689.
Parkinson disease (PD) is one of the most common neurological diseases. At present, because the exact cause is still unclear, accurate diagnosis and progression monitoring remain challenging. In recent years, exploring the relationship between PD and speech impairment has attracted widespread attention in the academic world. Most of the studies successfully validated the effectiveness of some vocal features. Moreover, the noninvasive nature of speech signal-based testing has pioneered a new way for telediagnosis and telemonitoring. In particular, there is an increasing demand for artificial intelligence-powered tools in the digital health era.
This study aimed to build a real-time speech signal analysis tool for PD diagnosis and severity assessment. Further, the underlying system should be flexible enough to integrate any machine learning or deep learning algorithm.
At its core, the system we built consists of two parts: (1) speech signal processing: both traditional and novel speech signal processing technologies have been employed for feature engineering, which can automatically extract a few linear and nonlinear dysphonia features, and (2) application of machine learning algorithms: some classical regression and classification algorithms from the machine learning field have been tested; we then chose the most efficient algorithms and relevant features.
Experimental results showed that our system had an outstanding ability to both diagnose and assess severity of PD. By using both linear and nonlinear dysphonia features, the accuracy reached 88.74% and recall reached 97.03% in the diagnosis task. Meanwhile, mean absolute error was 3.7699 in the assessment task. The system has already been deployed within a mobile app called No Pa.
This study performed diagnosis and severity assessment of PD from the perspective of speech order detection. The efficiency and effectiveness of the algorithms indirectly validated the practicality of the system. In particular, the system reflects the necessity of a publicly accessible PD diagnosis and assessment system that can perform telediagnosis and telemonitoring of PD. This system can also optimize doctors' decision-making processes regarding treatments.
帕金森病（PD）是最常见的神经疾病之一。目前，由于确切病因仍不清楚，准确诊断和病情进展监测仍然具有挑战性。近年来，探索PD与言语障碍之间的关系在学术界引起了广泛关注。大多数研究成功验证了一些嗓音特征的有效性。此外，基于语音信号测试的非侵入性为远程诊断和远程监测开创了一条新途径。特别是，在数字健康时代，对人工智能驱动工具的需求日益增长。
本研究旨在构建一个用于PD诊断和严重程度评估的实时语音信号分析工具。此外，底层系统应足够灵活，能够集成任何机器学习或深度学习算法。
我们构建的系统核心由两部分组成：（1）语音信号处理：采用传统和新颖的语音信号处理技术进行特征工程，可自动提取一些线性和非线性发声障碍特征；（2）机器学习算法应用：测试了机器学习领域的一些经典回归和分类算法，然后选择了最有效的算法和相关特征。
实验结果表明，我们的系统在PD诊断和严重程度评估方面具有出色的能力。通过使用线性和非线性发声障碍特征，在诊断任务中准确率达到88.74%，召回率达到97.03%。同时，在评估任务中平均绝对误差为3.7699。该系统已部署在一款名为No Pa的移动应用程序中。
本研究从语音指令检测的角度对PD进行了诊断和严重程度评估。算法的效率和有效性间接验证了系统的实用性。特别是，该系统体现了一个可公开访问的PD诊断和评估系统的必要性，该系统可以对PD进行远程诊断和远程监测。该系统还可以优化医生关于治疗的决策过程。
